Add train and test model prediction

ML/model_test.py

@@ -0,0 +1,31 @@
+import torch
+from model_train import MyNeuralNetwork, load_data
+from torch.utils.data import DataLoader
+import csv
+
+
+def main() -> None:
+    model: MyNeuralNetwork = MyNeuralNetwork()
+    model.load_state_dict(torch.load('model.pt'))
+    model.eval()
+    test_dataset = load_data("home_loan_test.csv")
+    batch_size: int = 32
+    test_dataloader = DataLoader(test_dataset, batch_size=batch_size)
+    predictions = []
+    labels = []
+    with torch.no_grad():
+        for batch_data, batch_labels in test_dataloader:
+            batch_predictions = model(batch_data)
+            predictions.extend(batch_predictions)
+            labels.extend(batch_labels)
+    filename = "results.csv"
+    column_name = "predict"
+    with open(filename, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow([column_name])
+        for result in predictions:
+            loan_decision = 1 if result.item() > 0.5 else 0
+            writer.writerow([loan_decision])
+    
+if __name__ == "__main__":
+    main()

ML/model_train.py

@@ -0,0 +1,133 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelBinarizer
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+
+
+
+class MyNeuralNetwork(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super(MyNeuralNetwork, self).__init__(*args, **kwargs)
+        self.fc1 = nn.Linear(12, 64)
+        self.relu = nn.ReLU()
+        self.fc1 = nn.Linear(12, 64)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(64, 1)
+        self.sigmoid = nn.Sigmoid()
+    
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return x
+
+def prepare_df_for_nn(df: pd.DataFrame):
+    
+    id_column_name_list: list[str] = [column for column in df.columns.to_list() if 'id' in  column.lower()]
+    if len(id_column_name_list) == 0:
+        pass
+    else:
+        df.drop(id_column_name_list[0], inplace=True, axis=1)
+    encoder: LabelBinarizer =  LabelBinarizer()
+    df.reset_index(inplace=True)
+    for column in df.columns:
+        if str(df[column].dtype).lower() == 'object':
+            encoded_column: np.ndarray = encoder.fit_transform(df[column])
+            df[column] = pd.Series(encoded_column.flatten(), dtype=pd.Int16Dtype)
+    return df
+
+def load_data(path: str):
+    df: pd.DataFrame = pd.read_csv('home_loan_train.csv') 
+    train_dataset: pd.DataFrame = prepare_df_for_nn(df)
+    x: np.ndarray = train_dataset.iloc[:, :-1].values.astype(float)
+    y: np.ndarray = train_dataset.iloc[:, -1].values.astype(float)
+    x_tensor: torch.Tensor = torch.tensor(x, dtype=torch.float32)
+    y_tensor: torch.Tensor = torch.tensor(y, dtype=torch.float32)
+    dataset: TensorDataset = TensorDataset(x_tensor, y_tensor)
+    return dataset
+
+def train(epochs: int, dataloader_train: DataLoader, dataloader_val: DataLoader):
+    model: MyNeuralNetwork = MyNeuralNetwork()
+    criterion: nn.BCELoss = nn.BCELoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    for epoch in range(epochs):
+        total_correct_train = 0
+        total_samples_train = 0
+        total_correct_val = 0
+        total_samples_val = 0
+
+        for inputs, labels in dataloader_train:
+            outputs = model(inputs)
+            labels = labels.reshape((labels.shape[0], 1))
+            loss = criterion(outputs, labels)
+            predicted_labels = (outputs > 0.5).float()
+            total_correct_train += (predicted_labels == labels).sum().item()
+            total_samples_train += labels.size(0)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        with torch.no_grad():
+            for inputs, labels in dataloader_val:
+                outputs_val = model(inputs)
+                predicted_labels_val = (outputs_val > 0.5).float()
+                labels = labels.reshape((labels.shape[0], 1))
+                total_correct_val += (predicted_labels_val == labels).sum().item()
+                total_samples_val += labels.size(0)
+
+        accuracy_val = total_correct_val / total_samples_val
+        accuracy_train = total_correct_train / total_samples_train
+        print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}, Accuracy train: {accuracy_train:.4f}, Accuracy val: {accuracy_val:.4f}")
+        
+    return model
+
+def create_dataset():
+    home_loan_train = pd.read_csv('/Users/wojciechbatruszewicz/InformatykaStudia/SEMESTR8/IUM/ZADANIA/createDataset/loan_sanction_train.csv')
+    home_loan_test = pd.read_csv('/Users/wojciechbatruszewicz/InformatykaStudia/SEMESTR8/IUM/ZADANIA/createDataset/loan_sanction_test.csv')
+
+    home_loan_train_final, home_loan_test = train_test_split(home_loan_train, test_size=0.2, random_state=1)
+    home_loan_test_final, home_loan_val_final = train_test_split(home_loan_test, test_size=0.5, random_state=1)
+
+    numeric_cols_train = home_loan_train_final.select_dtypes(include='number').columns
+    numeric_cols_test = home_loan_test_final.select_dtypes(include='number').columns
+    numeric_cols_val = home_loan_val_final.select_dtypes(include='number').columns
+
+    scaler = MinMaxScaler()
+
+    home_loan_train_final[numeric_cols_train] = scaler.fit_transform(home_loan_train_final[numeric_cols_train])
+    home_loan_test_final[numeric_cols_test] = scaler.fit_transform(home_loan_test_final[numeric_cols_test])
+    home_loan_val_final[numeric_cols_val] = scaler.fit_transform(home_loan_val_final[numeric_cols_val])
+    
+    home_loan_train_final = home_loan_train_final.dropna()
+    home_loan_test_final = home_loan_test_final.dropna()
+    home_loan_val_final = home_loan_val_final.dropna()
+
+    home_loan_train_final.to_csv('home_loan_train.csv', index=False)
+    home_loan_test_final.to_csv('home_loan_test.csv', index=False)
+    home_loan_val_final.to_csv('home_loan_val.csv', index=False)
+
+def main() -> None:
+    # create_dataset()
+    train_dataset = load_data("home_loan_train.csv")
+    val_dataset = load_data("home_loan_val.csv")
+    
+    batch_size: int = 32
+    dataloader_train = DataLoader(train_dataset, batch_size = batch_size, shuffle=True)
+    dataloader_val = DataLoader(val_dataset, batch_size = batch_size)
+        
+    model = train(20, dataloader_train, dataloader_val)
+    torch.save(model.state_dict(), 'model.pt')
+    
+if __name__ == "__main__":
+    main()
+    
+
+    

createDataset/createDataset.py

@@ -4,8 +4,8 @@ from sklearn.model_selection import train_test_split
 home_loan_train = pd.read_csv('loan_sanction_train.csv')
 home_loan_test = pd.read_csv('loan_sanction_test.csv')
 
-home_loan_val_final, home_loan_test_final = train_test_split(home_loan_test, test_size=0.5, random_state=1)
-home_loan_train_final = home_loan_train
+home_loan_train_final, home_loan_test = train_test_split(home_loan_train, test_size=0.2, random_state=1)
+home_loan_test_final, home_loan_val_final = train_test_split(home_loan_test, test_size=0.5, random_state=1)
 
 numeric_cols_train = home_loan_train_final.select_dtypes(include='number').columns
 numeric_cols_test = home_loan_test_final.select_dtypes(include='number').columns
@@ -17,6 +17,10 @@ home_loan_train_final[numeric_cols_train] = scaler.fit_transform(home_loan_train
 home_loan_test_final[numeric_cols_test] = scaler.fit_transform(home_loan_test_final[numeric_cols_test])
 home_loan_val_final[numeric_cols_val] = scaler.fit_transform(home_loan_val_final[numeric_cols_val])
 
+home_loan_train_final = home_loan_train_final.dropna()
+home_loan_test_final = home_loan_test_final.dropna()
+home_loan_val_final = home_loan_val_final.dropna()
+
 home_loan_train_final.to_csv('home_loan_train.csv', index=False)
 home_loan_test_final.to_csv('home_loan_test.csv', index=False)
 home_loan_val_final.to_csv('home_loan_val.csv', index=False)